code: def test_non_preprocessing_layers_1d(self):
      input_spec = tensor_spec.TensorSpec((32, 3), tf.float32)
      network = encoding_network.EncodingNetwork(
          input_spec,
          conv_layer_params=((16, 2, 1), (15, 2, 1)),
          fc_layer_params=(10, 5, 2),
          activation_fn=tf.keras.activations.tanh,
          conv_type="1d",
      )
  
      network.create_variables()
  
      variables = network.variables
      self.assertLen(variables, 10)
      self.assertLen(network.layers, 6)
  
      # Validate first conv layer.
      config = network.layers[0].get_config()
      self.assertEqual("tanh", config["activation"])
      self.assertEqual((2,), config["kernel_size"])
      self.assertEqual(16, config["filters"])
      self.assertEqual((1,), config["strides"])
      self.assertTrue(config["trainable"])
  
      # Validate second conv layer.
      config = network.layers[1].get_config()
      self.assertEqual("tanh", config["activation"])
      self.assertEqual((2,), config["kernel_size"])
      self.assertEqual(15, config["filters"])
      self.assertEqual((1,), config["strides"])
      self.assertTrue(config["trainable"])
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.networks import encoding_network
  from tf_agents.specs import tensor_spec
  from tf_agents.utils import test_utils
label_desc: Use image node_id 3 for calling the EncodingNetworkTest obj's underlying member method code with example usage: obj.test_non_preprocessing_layers_1d() without return types